It doesn’t take long for any data team to recognize the need for a productionization process to speed up the time-to-value of their models. Manually deploying models of any level of complexity again and again, fiddling with different technology and piecemealing tools on a per-model basis, relentless technical and stakeholder meetings, and endless roadblocks keep data experts frustrated and curb their ability to prove value.
However, look beyond that small data team to the long-term expectations of the enterprise. Not only are data teams expected to deploy cutting-edge machine learning models, but they’re expected to upskill the organization. And if they’re successful, together with IT, they will need to productionize and ensure governance of any data science solution – whether it’s the automation of data tasks, the deployment of data apps and reports, or the productionization of an inference service.
Maturing your data science practice means solving the business critical problems of today with modern, sophisticated data science – but also preparing your organization for the problems of tomorrow, which will inevitably require the deployment and governance of hundreds of solutions deployed across the upskilled, data-driven enterprise.
